Top Tech Jobs & Startup Jobs in San Francisco Bay Area, CA

Lead a data science team to design, build, and deploy large-scale, real-time AI/ML solutions (forecasting, diagnostics, LLM chatbot, retention models, optimization) to optimize GPU cloud gaming performance and user experience.

Design and implement an LLVM-based backend JIT compiler for NVIDIA GPUs. Develop compiler passes and optimizations, focusing on high performance and low compile time. Collaborate with hardware, compiler, and application teams to resolve issues and influence future GPU architectures.

Lead development and adoption of Warp by implementing CUDA C++ and Python integrations, turning CUDA features into supported functionality, integrating CUDA-X libraries, partnering with internal teams for production integration, and designing APIs and abstractions to expand simulation, optimization, and AI workloads.

Lead development and improvement of ASIC integration, build, simulation, CI, and debug infrastructure for high-speed IO GPU IP. Apply automation, metrics, and AI-assisted workflows to improve reliability, turnaround, and debuggability. Collaborate across design, verification, firmware, methodology, and infrastructure teams to root-cause failures and deliver durable process improvements.

Design, develop, and optimize GPU- and CPU-accelerated image and data-processing libraries. Perform performance tuning, benchmarking, and optimization across architectures; collaborate with product management and partners; provide technical leadership and guidance to library engineers; and improve library performance and maintainability.

Lead strategic sales and business development for NVIDIA Networking at a major Silicon Valley account. Build relationships with R&D and technical teams, drive adoption of GPU-accelerated and AI solutions, manage forecasting, supply and pricing, liaise between customers and product teams, and collaborate with engineering and solutions architects to qualify and deploy networking platforms. Travel up to 25%.

Design, build, and optimize GPU‑accelerated inference software and open‑source frameworks (vLLM, SGLang, FlashInfer). Improve performance and scale for LLMs and generative models across NVIDIA GPUs and edge SoCs using CUDA, CUTLASS, Triton, NCCL and profiling tools. Collaborate across teams to enable efficient model serving and deployment.

Design and build real-time, physics-informed AI models and synthetic data pipelines for quantum calibration and decoding. Develop surrogate models and GPU-accelerated implementations, apply reinforcement and online learning for calibration policies, co-design low-latency calibration-decoding systems, and collaborate across engineering and research to enable fault-tolerant quantum hardware.

The Senior Formal Verification Engineer will verify sophisticated ASIC builds using formal methods, collaborate with architecture teams, develop verification environments, and support silicon debug.

Lead architecture and global strategy for large-scale, low-latency distributed AI inference. Design high-throughput model serving, hardware-software co-optimization, orchestration for deployment/versioning/auto-scaling across cloud and datacenters, collaborate with customers and open-source ecosystems, and drive full software and system lifecycle for production reliability and performance.